Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Stratified Sampling Method01:16

Stratified Sampling Method

11.9K
Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a stratified sample, divide the population into groups called strata and then take a...
11.9K
Randomized Experiments01:13

Randomized Experiments

6.9K
The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...
6.9K
Sampling Plans01:23

Sampling Plans

180
Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
180
Strategies for Assessing and Addressing Confounding01:25

Strategies for Assessing and Addressing Confounding

92
Confounding is a critical issue in epidemiological studies, often leading to misleading conclusions about associations between exposures and outcomes. It occurs when the relationship between the exposure and the outcome is mixed with the effects of other factors that influence the outcome. Given that, addressing confounding is of high importance for drawing accurate inferences in research.
Confounding can be addressed at both the design phase of a study and through analytical methods after data...
92
Cluster Sampling Method01:20

Cluster Sampling Method

11.9K
Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
11.9K
Group Design02:01

Group Design

8.9K
The most basic experimental design involves two groups: the experimental group and the control group. The two groups are designed to be the same except for one difference— experimental manipulation. The experimental group gets the experimental manipulation—that is, the treatment or variable being tested—and the control group does not. Since experimental manipulation is the only difference between the experimental and control groups, we can be sure that any differences between...
8.9K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Rethinking risk in Crohn's surgery: age at onset fails to predict surgical outcomes after ileocecal resection, insights from a tertiary referral center.

Techniques in coloproctology·2026
Same author

Reliability of bioimpedance spectroscopy in the upper trapezius muscle.

Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology·2026
Same author

A historical note: Rediscovering an unpublished response to Korn and Freidlin (2011).

Statistical methods in medical research·2026
Same author

Revisiting optimal allocations for binary responses: insights from considering type-I error rate control.

Biometrics·2025
Same author

Comparative analysis of robotic, laparoscopic, and open ileal pouch-anal anastomosis outcomes: retrospective cohort study.

BJS open·2025
Same author

Mortality from Pleural and Lung Cancer in Railway Maintenance Workers.

Life (Basel, Switzerland)·2025
Same journal

Impact of Information Leakage in Platform Trials With Survival Endpoints on Type I Error Control.

Pharmaceutical statistics·2026
Same journal

Harmonic Fowlkes-Mallows Index for Medical Diagnostics Tests and Optimal Cut-Off Point Selection of Binary Diseases.

Pharmaceutical statistics·2026
Same journal

Early Phase Dose-Finding Designs for CAR-T Cell Therapies.

Pharmaceutical statistics·2026
Same journal

Optimizing Randomization Ratios in Clinical Trials With Survival Endpoints.

Pharmaceutical statistics·2026
Same journal

CUI-MET: A Clinical Utility Index Based Analysis and Decision Framework for Dose Optimization in Multiple-Dose, Multiple-Outcome Randomized Trials.

Pharmaceutical statistics·2026
Same journal

Will the Pharmaceutical Industry Need Statisticians in an AI World?

Pharmaceutical statistics·2026
See all related articles

Related Experiment Video

Updated: Jun 21, 2025

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

14.4K

Exploring Stratification Strategies for Population- Versus Randomization-Based Inference.

Marco Novelli1, William F Rosenberger2

  • 1Department of Statistics Bologna, University of Bologna, Bologna, Italy.

Pharmaceutical Statistics
|July 10, 2024
PubMed
Summary
This summary is machine-generated.

Prestratification in clinical trials offers minimal benefit and can reduce inferential precision, especially with randomization-based methods. This strategy may even be detrimental, impacting the credibility of experimental results.

Keywords:
chronological biaspoststratificationprestratificationrandomization testsregression adjustmentsubgroup analysis

More Related Videos

Sampling Strategies and Processing of Biobank Tissue Samples from Porcine Biomedical Models
05:07

Sampling Strategies and Processing of Biobank Tissue Samples from Porcine Biomedical Models

Published on: March 6, 2018

15.6K
Barnes Maze Testing Strategies with Small and Large Rodent Models
12:59

Barnes Maze Testing Strategies with Small and Large Rodent Models

Published on: February 26, 2014

41.9K

Related Experiment Videos

Last Updated: Jun 21, 2025

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

14.4K
Sampling Strategies and Processing of Biobank Tissue Samples from Porcine Biomedical Models
05:07

Sampling Strategies and Processing of Biobank Tissue Samples from Porcine Biomedical Models

Published on: March 6, 2018

15.6K
Barnes Maze Testing Strategies with Small and Large Rodent Models
12:59

Barnes Maze Testing Strategies with Small and Large Rodent Models

Published on: February 26, 2014

41.9K

Area of Science:

  • Clinical Trials Methodology
  • Biostatistics
  • Experimental Design

Background:

  • Stratification is a common clinical trial practice to ensure baseline covariate balance.
  • The actual benefits of stratification on inferential precision remain debated in scientific literature.
  • Previous studies suggest limited or negligible efficiency gains from stratification, particularly in large samples.

Purpose of the Study:

  • To investigate subgroup analysis strategies in clinical trials.
  • To compare the inferential precision of prestratification against poststratification and post hoc regression adjustment.
  • To discuss population-based versus randomization-based inference for each approach.

Main Methods:

  • Comparative analysis of prestratification, poststratification, and regression adjustment.
  • Evaluation of clinical trial strategies considering treatment-by-covariate interactions.
  • Assessment of inference methods including population-based and randomization-based approaches.

Main Results:

  • Prestratification generally provides no substantial inferential benefit in clinical trials.
  • Prestratification can be detrimental, particularly for randomization-based inference with chronological bias.
  • Even with treatment-by-covariate interactions, prestratification may decrease inferential precision.

Conclusions:

  • The routine use of prestratification in clinical trials is not generally recommended.
  • Alternative methods like poststratification or regression adjustment may offer better inferential precision.
  • Careful consideration of potential biases and interactions is crucial when selecting subgroup analysis strategies.